ActiveSplat: High-Fidelity Scene Reconstruction through Active Gaussian Splatting

• URL: http://arxiv.org/abs/2410.21955v1
• Date: Tue, 29 Oct 2024 11:18:04 GMT
• Title: ActiveSplat: High-Fidelity Scene Reconstruction through Active Gaussian Splatting
• Authors: Yuetao Li, Zijia Kuang, Ting Li, Guyue Zhou, Shaohui Zhang, Zike Yan,
• Abstract summary: We propose ActiveSplat, an autonomous high-fidelity reconstruction system leveraging Gaussian splatting. The system establishes a unified framework for online mapping, viewpoint selection, and path planning. Experiments and ablation studies validate the efficacy of the proposed method in terms of reconstruction accuracy, data coverage, and exploration efficiency.
• Score: 12.628559736243536
• License:
• Abstract: We propose ActiveSplat, an autonomous high-fidelity reconstruction system leveraging Gaussian splatting. Taking advantage of efficient and realistic rendering, the system establishes a unified framework for online mapping, viewpoint selection, and path planning. The key to ActiveSplat is a hybrid map representation that integrates both dense information about the environment and a sparse abstraction of the workspace. Therefore, the system leverages sparse topology for efficient viewpoint sampling and path planning, while exploiting view-dependent dense prediction for viewpoint selection, facilitating efficient decision-making with promising accuracy and completeness. A hierarchical planning strategy based on the topological map is adopted to mitigate repetitive trajectories and improve local granularity given limited budgets, ensuring high-fidelity reconstruction with photorealistic view synthesis. Extensive experiments and ablation studies validate the efficacy of the proposed method in terms of reconstruction accuracy, data coverage, and exploration efficiency. Project page: https://li-yuetao.github.io/ActiveSplat/.

Related papers

• LiteVLoc: Map-Lite Visual Localization for Image Goal Navigation [5.739362282280063]
  LiteVLoc is a visual localization framework that uses a lightweight topo-metric map to represent the environment. It reduces storage overhead by leveraging learning-based feature matching and geometric solvers for metric pose estimation.
  arXiv  Detail & Related papers  (2024-10-06T09:26:07Z)
• RGBD GS-ICP SLAM [1.3108652488669732]
  We propose a novel dense representation SLAM approach with a fusion of Generalized Iterative Closest Point (G-ICP) and 3D Gaussian Splatting (3DGS) Experimental results demonstrate the effectiveness of our approach, showing an incredibly fast speed up to 107 FPS.
  arXiv  Detail & Related papers  (2024-03-19T08:49:48Z)
• Mapping High-level Semantic Regions in Indoor Environments without Object Recognition [50.624970503498226]
  The present work proposes a method for semantic region mapping via embodied navigation in indoor environments. To enable region identification, the method uses a vision-to-language model to provide scene information for mapping. By projecting egocentric scene understanding into the global frame, the proposed method generates a semantic map as a distribution over possible region labels at each location.
  arXiv  Detail & Related papers  (2024-03-11T18:09:50Z)
• PAS-SLAM: A Visual SLAM System for Planar Ambiguous Scenes [41.47703182059505]
  We propose a visual SLAM system based on planar features designed for planar ambiguous scenes. We present an integrated data association strategy that combines plane parameters, semantic information, projection IoU, and non-parametric tests. Finally, we design a set of multi-constraint factor graphs for camera pose optimization.
  arXiv  Detail & Related papers  (2024-02-09T01:34:26Z)
• LoLep: Single-View View Synthesis with Locally-Learned Planes and Self-Attention Occlusion Inference [66.45326873274908]
  We propose a novel method, LoLep, which regresses Locally-Learned planes from a single RGB image to represent scenes accurately. Compared to MINE, our approach has an LPIPS reduction of 4.8%-9.0% and an RV reduction of 73.9%-83.5%.
  arXiv  Detail & Related papers  (2023-07-23T03:38:55Z)
• Active Implicit Object Reconstruction using Uncertainty-guided Next-Best-View Optimization [1.2268315442962412]
  Actively planning sensor views during object reconstruction is crucial for autonomous mobile robots. We propose a seamless integration of the emerging implicit representation with the active reconstruction task. Our approach effectively improves reconstruction accuracy and efficiency of view planning in active reconstruction tasks.
  arXiv  Detail & Related papers  (2023-03-29T14:42:30Z)
• ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine Refinement [80.94378602238432]
  We propose an efficient structure named Correspondence Efficient Transformer (ECO-TR) by finding correspondences in a coarse-to-fine manner. To achieve this, multiple transformer blocks are stage-wisely connected to gradually refine the predicted coordinates. Experiments on various sparse and dense matching tasks demonstrate the superiority of our method in both efficiency and effectiveness against existing state-of-the-arts.
  arXiv  Detail & Related papers  (2022-09-25T13:05:33Z)
• Lightweight Object-level Topological Semantic Mapping and Long-term Global Localization based on Graph Matching [19.706907816202946]
  We present a novel lightweight object-level mapping and localization method with high accuracy and robustness. We use object-level features with both semantic and geometric information to model landmarks in the environment. Based on the proposed map, the robust localization is achieved by constructing a novel local semantic scene graph descriptor.
  arXiv  Detail & Related papers  (2022-01-16T05:47:07Z)
• Learning Space Partitions for Path Planning [54.475949279050596]
  PlaLaM outperforms existing path planning methods in 2D navigation tasks, especially in the presence of difficult-to-escape local optima. These gains transfer to highly multimodal real-world tasks, where we outperform strong baselines in compiler phase ordering by up to 245% and in molecular design by up to 0.4 on properties on a 0-1 scale.
  arXiv  Detail & Related papers  (2021-06-19T18:06:11Z)
• Bottom-up mechanism and improved contract net protocol for the dynamic task planning of heterogeneous Earth observation resources [61.75759893720484]
  Earth observation resources are becoming increasingly indispensable in disaster relief, damage assessment and related domains. Many unpredicted factors, such as the change of observation task requirements, to the occurring of bad weather and resource failures, may cause the scheduled observation scheme to become infeasible. A bottom-up distributed coordinated framework together with an improved contract net are proposed to facilitate the dynamic task replanning for heterogeneous Earth observation resources.
  arXiv  Detail & Related papers  (2020-07-13T03:51:08Z)
• Plan2Vec: Unsupervised Representation Learning by Latent Plans [106.37274654231659]
  We introduce plan2vec, an unsupervised representation learning approach that is inspired by reinforcement learning. Plan2vec constructs a weighted graph on an image dataset using near-neighbor distances, and then extrapolates this local metric to a global embedding by distilling path-integral over planned path. We demonstrate the effectiveness of plan2vec on one simulated and two challenging real-world image datasets.
  arXiv  Detail & Related papers  (2020-05-07T17:52:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.